Probability mapping of soil thickness by random survival forest at a national scale
Sign Up to like & getrecommendations! Published in 2019 at "Geoderma"
DOI: 10.1016/j.geoderma.2019.03.016
Abstract: Soil thickness (ST) is a crucial factor in earth surface modelling and soil storage capacity calculations (e.g., available water capacity and carbon stocks). However, the observed depths recorded in soil information systems for some profiles… read more here.
Keywords: censored data; soil thickness; soil; right censored ... See more keywords
Identification of important risk factors for all-cause mortality of acquired long QT syndrome patients using random survival forests and non-negative matrix factorization.
Sign Up to like & getrecommendations! Published in 2020 at "Heart rhythm"
DOI: 10.1016/j.hrthm.2020.10.022
Abstract: BACKGROUND Acquired long QT syndrome (aLQTS) is often associated with poor clinical outcomes. OBJECTIVE The present study examined the important predictors for all-cause mortality of aLQTS patients by applying both random survival forest (RSF) and… read more here.
Keywords: long syndrome; cause mortality; random survival; mortality ... See more keywords
Prediction the survival of patients with breast cancer using random survival forests for competing risks
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Preventive Medicine and Hygiene"
DOI: 10.15167/2421-4248/jpmh2022.63.2.2405
Abstract: Summary Objectives Breast cancer (BC) is the most common cause of cancer death in Iranian women. Sometimes death from other causes precludes the event of interest and makes the analysis complicated. The purpose of this… read more here.
Keywords: using random; breast cancer; model; random survival ... See more keywords
Development and Validation of a New Multiparametric Random Survival Forest Predictive Model for Breast Cancer Recurrence with a Potential Benefit to Individual Outcomes
Sign Up to like & getrecommendations! Published in 2022 at "Cancer Management and Research"
DOI: 10.2147/cmar.s346871
Abstract: Purpose Breast cancer (BC) is a multi-factorial disease. Its individual prognosis varies; thus, individualized patient profiling is instrumental to improving BC management and individual outcomes. An economical, multiparametric, and practical model to predict BC recurrence… read more here.
Keywords: breast cancer; recurrence; individual outcomes; model ... See more keywords
A Comparison Study of Machine Learning (Random Survival Forest) and Classic Statistic (Cox Proportional Hazards) for Predicting Progression in High-Grade Glioma after Proton and Carbon Ion Radiotherapy
Sign Up to like & getrecommendations! Published in 2020 at "Frontiers in Oncology"
DOI: 10.3389/fonc.2020.551420
Abstract: Background Machine learning (ML) algorithms are increasingly explored in glioma prognostication. Random survival forest (RSF) is a common ML approach in analyzing time-to-event survival data. However, it is controversial which method between RSF and traditional… read more here.
Keywords: machine learning; grade; study; model ... See more keywords